Entrepreneurial Saving Practices and Reinvestment: Theory
and Evidence from Tanzanian MSEs∗
Thorsten Beck †
Cass Business School, City University London
Tilburg University
CEPR
Haki Pamuk ‡
Tilburg University
Burak R. Uras §
Tilburg University
May 31, 2014
Abstract
What is the relationship between entrepreneurial saving practices and reinvestment? We develop a
model of entrepreneurial finance and show that entrepreneurial reinvestment decisions depend on
the efficiency of saving practices. Utilizing a novel micro & small enterprise survey from Tanzania
we test the empirical implications of this theory. We find (1) saving for business purposes and
earnings reinvestment are positively related; (2) the practice of saving in a deposit account of a
formal financial institution is more likely to facilitate reinvestment compared to the practice of
keeping savings within the household. We also show that the negative impact of saving withinthe-household on investment is more pronounced for family members with inherently low intrahousehold bargaining power - such as females and non-head household members. Our work
contributes to the recent debate on the implications of saving instruments in developing countries,
and suggests informal saving practices as potential barriers to microenterprise performance.
Keywords: Micro- and small enterprises; savings; reinvestment; Tanzania.
JEL Classification: D14; G21; O12; O16.
∗
We wish to express our gratitude to the Financial Sector Deepening Trust (FSDT), Tanzania for kindly providing
us with the main data set used in the study. We would like to thank Daan van Soest, Erwin Bulte, Matreesh Ghatak,
Michele Tertilt, Silvio Daidone, Benedikt Goderis, Gonzague Vannorenberghe, Bert Willems, Christian Mpalanzi and
FSD Tanzania, and seminar participants at Tilburg Economics Workshop and CSAE Conference 2014 for valuable
comments and suggestions. Haki Pamuk also would like to thank N.W.O. for financial support (N.W.O. grant number
453-10-001).This research was funded with support from the Department for International Development (DFID) in the
framework of the research project ‘Co-ordinated Country Case Studies: Innovation and Growth, Raising Productivity
in Developing Countries’. All remaining errors are ours.
†
E-mail: t.beck@uvt.nl
‡
E-mail: h.pamuk@uvt.nl
§
Corresponding Author. E-mail: r.b.uras@uvt.nl
1
1
Introduction
In developing countries, intermediation costs and enforcement frictions constrain access to external
finance by micro and small enterprises (MSEs) - leaving entrepreneurs’ earning retention as a key
element for small business growth. But, what explains entrepreneurial decisions to reinvest in their
own businesses? Given the limited access to formal financial services, many entrepreneurs use informal
mechanisms of saving and liquidity management to facilitate their earnings retention. In this paper, we
utilize a novel dataset from Tanzania to explore whether entrepreneurial saving practices can explain
variation in entrepreneurs’ reinvestment decisions. Specifically, we gauge whether the decision to save
with formal financial institutions, individually (under the mattress), within the household or via other
informal arrangements, such as rotating savings and credit associations (ROSCAs), affect the decision
to reinvest entrepreneurial earnings. We motivate our empirical work with a simple theoretical model
that shows that an entrepreneur’s reinvestment decision depends on the entrepreneur’s saving practice,
in addition to productivity and borrowing capacity of her entrepreneurial firm.
In the absence of easy access to external finance, saving for business purposes should be positively correlated with entrepreneurial investment. However, the saving mechanism itself might be a
critical element in determining the ability to reinvest. On the one hand, for formal savers the opportunity cost of consuming savings instead of reinvesting them is not only the loss of financial reserves
but also the foregone interest income. On the other hand, for instance, the “within-household savers”
might be less likely to reinvest, because they suffer from the redistributive pressure resulting from
the saved funds being held inside the household. If the remaining household members are aware of
the existence of entrepreneurial savings, it can be hard to prevent the funds from being exploited for
the general consumption needs of the household. In addition to these two extreme cases, we could
also think of the “individual savers” and the “informal finance network savers”as other saving practice types. Comparing “individual savers” with “informal network savers”, we note that although
the interest income from informal finance networks should have a positive impact on the opportunity
cost of consumption and foster investment, the inflexibility to withdraw savings at informal financial
institutions might offset this income effect and reduce the earnings retention.1
1
The rate of return to savings in social saving clubs is typically lower compared to formal financial institutions. For
2
In order to inform our empirical hypotheses, at first, we present a simple theoretical model to
explain the relationship between entrepreneurial investment decisions and saving practices. We show
that entrepreneurs are more likely to invest in their businesses if they save in a fashion which allows
them easy access to their funds, such as formal savings accounts or personal saving mechanisms.
To test the empirical relationship between savings patterns and entrepreneurial reinvestment
decisions, we use an MSE survey for over 6,000 entrepreneurs undertaken in 2010 in Tanzania. The
sample of entrepreneurs surveyed covers a large variety of enterprises in different locations, of different
gender, educational profile and sectors. We document that entrepreneurs’ saving practices do indeed
co-vary with the likelihood of earnings retention at MSEs. The survey design allows us to differentiate
between different savings vehicles, including within household saving, saving under the pillow, informal
savings clubs, and formal deposit accounts. Our results reveal that the probability of reinvestment
is significantly higher for savers and that when compared against formal deposit account holders,
entrepreneurs who give their savings to other household members to keep them safe are significantly
less likely to reinvest. Specifically, we find that when we compare the practice of keeping savings within
the household against the practice of having a deposit account at a formal financial institution, the
latter is more likely to be associated with reinvestment than the former.
We conduct a series of checks to ensure the robustness of our results to the inclusion of additional control variables and alternative model specifications. Furthermore, to address the potential
reverse causation of high reinvestment on saving practices we utilize the distance to the nearest bank
and entrepreneur’s age as instruments in recursive bivariate probit regressions. We use these two instruments, because accessibility to a bank and entrepreneur’s age can explain whether the savings will
be kept in a bank account or shared with the rest of the household, but these two variables are not
directly associated with reinvestment decisions. The coefficient estimates in the instrumental variable
regressions remain stable and significant across all specifications. Finally, we explore the differential effects of saving patterns on reinvestment decision across groups with different intra-household
related discussion see Vonderlack and Schreiner (2002). Entrepreneurs saving via informal channels are more likely to
have limited access to their savings. For instance, members of ROSCAs cannot access their savings until their turn
comes (see Besley et al. (1993) for a theoretical discussion of ROSCAs), unless there is a relevant secondary market
(Calorimis and Rajamaran, 1998). Similarly, moneylenders may postpone repaying the savings or it might be hard to
reach them.
3
bargaining power. We find that the negative relationship between saving within the household and
reinvestment decisions is stronger for entrepreneurs with lower intra-household bargaining power, such
as females and non-household heads.
Tanzania is a perfect setting to test the relationship between different saving practices and
entrepreneurial investment decisions. Tanzania is a low-income country in East Africa, whose private
sector is dominated by micro- and small enterprises. While the financial sector was liberalized in the
1990s and there is a large number of formal financial institutions, access to formal financial services is
very low, with only 17% of adults having a formal bank account (World Bank, 2012). Tanzania shares
many characteristics with other low-income countries in Africa, including a very disperse population
and a high degree of informality.
This paper relates to several distinct literatures. First of all, our study investigates the role of
saving practices on business investment. Past research on finance and entrepreneurial investment has
shown that entrepreneurs invest more if they expect high private returns from their investment activity
(e.g. Demirguc-Kunt and Maksimovic, 1998; Johnson, McMillan and Woodruff, 1998). Moreover,
there are several studies investigating the impact of access to external finance on investment for
microenterprises (Karlan and Zinman, 2010a; Karlan and Zinman, 2010b; Kaboski and Townsend,
2011; Attanasio et al., 2012 and Banerjee et al., 2013). We add to this literature by focusing on
savings patterns as additional factors explaining the variation in reinvestment decisions across microand small entrepreneurs.
Our most important contribution is to the growing literature concerning the implications of
access to different saving instruments in developing countries. There is an increasing number of
studies exploring the impact of access to formal banking services on the level of savings (Burgess and
Panda, 2005; Kaboski and Townsed, 2005; Dupas and Robinson, 2013a). A recent experimental study
by Dupas and Robinson (2013a) shows that entrepreneurs with formal bank accounts save and invest
more in their businesses than entrepreneurs who do not save in formal banks. In a companion study
(Dupas and Robinson, 2013b), the authors compare the health investment performance of women
saving via various informal saving instruments and find that some of them boost investment in health.
Similarly Brune et al. (2013) evaluate the effect of commitment to keep savings accounts on several
4
outcomes for Malawian cash crop farmers. We contribute to this literature by comparing the investment
likelihood of formal savers with different types of informal savers such as individual savers, savers via
other household members, informal savings club members and moneylenders.
Our paper also relates to the literature on barriers to saving in developing countries (see Karlan,
Ratan and Zinman, 2013, for an overview). In addition to geographic, monetary and regulatory
barriers, there are significant social constraints on saving behavior, partly related to the position of
the entrepreneur within the household. Previous research has linked participation in informal savings
clubs, such as ROSCAs, to intra-household bargaining problems (e.g., Besley et al. 1993; Anderson
and Baland, 2002). Social constraints can also explain why entrepreneurs save and borrow at the
same time. Critically, the literature has shown that the relative position within the household is
important for saving and investment decisions. For instance, de Mel et al. (2008) show that as the
decision making power of women in the household increases, returns to capital and investment for
women increase as well. Ashraf (2009) in a lab experiment in Philippines documents that subjects
are more likely to save the randomly allocated money in their private deposit accounts if their spouse
is not aware of the money, while they prefer to consume if the spouse knows about it. Evidence
from an experimental study with 142 married couples in Kenya showed that husbands increase private
spending if they receive an income shock. But if their wives receive the shock they do not increase their
consumption (Robinson 2011). Likewise Schaner (2013) finds that well matched Kenyan couples are
more likely to use joint accounts instead of costly individual ones. Our study supports these findings
by showing that members of the household who have potentially less power in decision making are less
likely to turn their household savings into investments.
Unlike many other papers in this literature that discuss randomized control trials (RCTs), our
paper relies on cross-sectional survey data and thus faces the usual endogeneity biases. We address
these concerns by using instrumental variables and by exploring the differential relationship between
savings patterns and reinvestment decision across different entrepreneurial groups. Beyond these
methodological differences; however, our analysis also allows a broader exploration of reinvestment
decisions across different savings patterns. In addition, we realize that such savings patterns are the
outcome of repeated interactions and persistent habits and are thus harder if not impossible to control
5
under a randomized control trial.
The rest of the paper is organized as follows. Section 2 presents a theoretical model to show how
saving practices can influence entrepreneurial investment decisions. Section 3 discusses the regression
set-up and the set of control variables. Section 4 presents the data we use for our analysis. Section
5 discusses our main findings, while section 6 discusses the determinants of saving choice, tests for
reverse causality and studies sub-sample heterogeneity concerning our key estimation results. Section
7 concludes.
2
A 2-Period Model
We develop a partial equilibrium heterogeneous firms model to study the interactions between entrepreneurial saving practices and profit reinvestment. In our model entrepreneurial heterogeneity has
three dimensions: productivity, borrowing capacity, and saving practice. In the benchmark model all
of the three dimensions are exogenous. We also extend the benchmark model in section 2.5, where
we endogenize the saving practice as an entrepreneurial decision. In the following, we first present
the economic environment, and then the entrepreneur’s maximization problem, before deriving the
optimal investment behavior. This allows us to obtain several empirically testable hypotheses.
2.1
Environment
There are two time periods, 1 and 2 ; a continuum of entrepreneurs indexed by i; and a good - call
it cash - that can be invested, saved or consumed. Entrepreneurs have linear preferences over the
life-time consumption such that
Ui = c1,i + βc2,i ,
(1)
where U is the life-time utility and c1 and c2 are consumption levels in period-1 and in period-2
respectively. The parameter β is a discount factor. The linear preference specification is not essential
for the qualitative findings of the model. It allows us to solve for the investment likelihood of the
6
entrepreneur as we will present in equations (11) and (12) below.
The realization of the investment cash-flow from entrepreneurial technology is conditional on
a liquidity injection that needs to be incurred at the beginning of period-2. Specifically, entrepreneur
i’s technology yields Ai k1,i units of cash in period-2 plus an additional L(k1,i ) if and only if the
entrepreneur is capable of injecting an L(k1,i ) at the beginning of the period-2 that is greater than
`2 k1,i . The parameter Ai > 1 captures the productivity heterogeneity across entrepreneurs. A high
Ai can be associated with better training, education or some sort of intrinsic ability to manage a
firm. We assume that A is drawn independently and identically from a distribution at the beginning
of the period-1. The variable k1,i is the capital investment of the entrepreneur in period-1 into the
productive investment opportunity. In this economy, firms must have the capacity to manage liquid
reserves in order to be able to undertake productive investment opportunities.2 The liquidity need of
the firm, `2 k1,i poses an inefficient use of capital: Every unit capital saved for liquidity purposes does
not get to invested into the productive investment opportunity. In this two-period model the liquidity
management capacity of the entrepreneur influences his capital investment decisions.
To summarize, the entrepreneurial total output at the end of the period-2, which we denote
with y2,i , has the following specification:
y2,i = Ai k1,i + L(k1,i ) if L(k1,i ) ≥ `2 k1,i ,
= L(k1,i ) if L(k1,i ) < `2 k1,i .
(2)
In this production function formulation, `2 ki captures the expected liquidity needs - for instance
working finance requirements of the business - whose size does not affect the return on investment
projects as long as it can be financed at the beginning of period-2.3
The capital investment in period-1 is financed by the entrepreneur’s endowment ω - which we
assume to be homogeneously distributed among all entrepreneurs in the economy. We do not allow for
2
We assume that `2 is a common parameter among all firms in the economy. The qualitative features of the model
would remain identical if we assumed heterogeneity and stochasticity in liquidity demand.
3
This type of a production function specification has been previously utilized in finance and development literature
by Aghion et al. (2010): In their dynamic general equilibrium model, the authors introduce a complementarity between
the ability to cope with future liquidity needs and current long-term investment and explain the negative correlation
between volatility and growth observed in the cross-country data.
7
borrowing at period-1 capital investment stage because our aim is to understand the dynamics behind
the probability of earnings retention and thus reinvestment at entrepreneurial firms. In this respect ω
captures entrepreneurial earnings before an investment decision is undertaken.
The liquidity need L can be financed via two sources:
1. The entrepreneur can borrow, denote it with b2,i , up to a θi fraction of L in the financial market
at a gross interest rate 1, where θi is an entrepreneur specific parameter capturing the ability to
raise working capital finance externally. The borrowing capacity θ is drawn from a distribution
function at the beginning of the period-1.
2. The entrepreneur can save cash from period-1 to period-2, which we will call saving for business
purposes denoted by s1,i , at a rate ζi with ζi ≤ 1. In this formulation, ζi captures saving practice
(in)efficiency of the entrepreneur. We assume that there are two general saving practice types:
Formal (ζF ) and informal (ζI ) - to be endogenized in section 2.5. We suppose that ζF = 1
for those who save formally, whereas ζI is drawn from a distribution function with ζI < 1.
The heterogeneity in informal saving (in)efficiency can be motivated, for instance, by the crosssectional variation in within-household bargaining power, as we will discuss below.
To summarize, the timing of events in both periods is specified as the following:
I. Period-1
1. Entrepreneurial (3-dimensional) types are realized.
2. Capital investment into the production technology.
3. Saving for business purposes.
4. Period-1 consumption.
I. Period-2
1. Borrowing to finance liquidity needs.
8
2. Liquidity injection: Using borrowed funds and savings from period-1.
3. Cash-flow realization from the production technology.
4. Loan repayment.
5. Period-2 consumption
We would like to note that in this model the exact timing of k investment is not too essential.
All we need is that k is invested before the liquidity injection is made. This means allowing parts of
the saving for business purposes s to finance k, which can be reached by changing the timing of events
(2) and (3) in period-1 timeline, will not alter the qualitative properties of the model that we highlight
in section 2.4.
2.2
Feasibility Constraints
The endogenous variables in this model are c1,i , c2,i , k1,i , and s1,i . Entrepreneurs maximize life-time
preferences delineated at (1) - with respect to the endogenous variables - subject to
c1,i + k1,i + s1,i ≤ ωi ,
c2,i ≤ Ai k1,i + s1,i ζi ,
(3)
(4)
where (3) and (4) are the budget constraints for period-1 and period-2 respectively. We would like to
note that `2 k1,i enters both sides of the constraint (4); and hence, gets cancelled out.
An immediate implication of this model can be summarized with the following.
Lemma 2.1 If and only if k1,i > 0, the entrepreneur forecasts that there will be sufficient capacity
to finance future liquidity needs. Therefore, the entrepreneur sets k1,i = 0 if his capacity to finance
liquidity is sufficiently low.
9
This result implies that as long as k1,i > 0 we have two additional constraints that need to
hold:
L(k1,i ) ≤ s1,i ζi + b2,i ,
θi L(k1,i ) ≥ b2,i .
(5)
(6)
The inequality (5) is the constraint that ensures that there is sufficient liquidity at the beginning of
the period-2 - financed by savings for business purposes (si ζi ) and borrowing (bi ). The inequality (6)
is the borrowing constraint associated with working capital finance.
2.3
Optimizing Behavior
The qualitative properties of this model are then as follows. Entrepreneurs who choose a k1,i > 0,
exhaust their borrowing limit θi . This is implied by the assumption that saving is inefficient (ζi <
ζF = 1) in this economy for informal type of saving practices. Therefore,
b2,i = θi L(k1,i ),
(7)
as long as ζi < 1.
Then using (5) with equality we get:
s1,i =
1 − θi
ζi
L(k1,i ).
(8)
Equation (8) implies that the lower ζ the higher is the amount of savings for business purposes - for
those entrepreneurs who choose to invest. But, as we show below a low ζ implies a low likelihood of
earnings retention and as a result a low likelihood of saving for business purposes.
10
Using (8) in budget constraints (3) and (4) yields:
c1 = ωi − k1,i −
1 − θi
ζi
L(k1,i ),
c2 = Ai k1,i + (1 − θi )L(k1,i ).
(9)
(10)
Letting the idiosyncratic rate of return from postponing consumption from period-1 to period-2
be denoted with ρi , the optimal consumption plans implied by (1) are described as:
1
,
β
1
> 0 if ρi > .
β
c1,i > 0, c2,i = 0 if ρi <
c1,i = 0, c2,i
(11)
Finally, using (9), (10) and the optimal consumption plans from (11) we can show that the
entrepreneur chooses to invest (k1,i > 0) in period-1 if and only if:
ρi ≡
Ai + (1 − θi )`2
1
>
β
i
1 + `2 1−θ
ζi
(12)
The left hand side of the inequality (12) is the unit rate of return from undertaking an investment
project for an entrepreneur i. The right hand side is the unit cost of postponing consumption from
period-1 to period-2. The entrepreneurs with high enough ρ - ρi > 1/β - invest in their projects
and consume the investment returns at the end of the period-2. When ρi is lower than 1/β, the
entrepreneur does not invest and consumes the endowment ω at the end of the period-1.
2.4
Empirically testable implications of the model
Applying comparative statics at (12) we capture the key empirically testable implication of the model
in the following proposition:
Proposition 2.2 Entrepreneurs with an efficient saving practice (high ζi ) are more likely to invest.
11
Proof Taking the first-partial derivative of ρ with respect to ζ we can see that
∂ρ
=
∂ζ
1
ζ2


`2 (1 − θ)(A + (1 − θ)`2 ) 
> 0,
h
i2


1−θ


1 + `2 ζ



which implies that the rate of return from investing rises with the efficiency of the saving
practice of the entrepreneur. A higher efficiency of an entrepreneur’s saving practice therefore also
raises the likelihood of earnings reinvestment, the key hypothesis of our empirical analysis. In order to deepen the empirical validity of our theoretical model, we also provide the following two
propositions.
Proposition 2.3 Entrepreneurs with a high borrowing capacity (high θi ) are more likely to invest.
Proof Defining z ≡
1−θ
ζ2
and taking the first-partial derivative of ρ with respect to θ:
`2
(A − 1)
∂ρ
> 0,
= z
∂θ
[1 + `2 ζz]2
which implies that the rate of return from investing rises with the entrepreneur’s borrowing capacity.
Proposition 2.4 Productive entrepreneurs (high Ai ) are more likely to invest.
Proof Taking the first-partial derivative of ρ with respect to A
∂ρ
1
=
> 0,
∂A
1 + `2 ζz
where again z ≡
1−θ
ζ2
, shows that the rate of return from investment rises with entrepreneurial ability.
12
2.5
Endogenizing the Saving Practice
Our theoretical model implies that if an entrepreneur’s saving practice is inefficient, then she is induced
to save a lot which makes investment, or in other words postponing consumption between period-1
and period-2, harder. Therefore, the saving practice of an entrepreneur is likely to be an endogenous
variable, where the decision to save formally might be a costly action.
To formalize this argument, suppose that there are two saving options available for an entrepreneur as spelled out previously - formal and informal. In order to be able to save formally the
entrepreneur needs to sacrifice a utility loss worth of ψi units of consumption for each unit of fund
deposited formally. This basically implies that formal savings impose a non-monetary cost for a class
of agents. The utility loss might be due to social costs (e.g. hiding savings from family members
at a bank account) or physical costs (e.g. transportation costs) as well as idiosyncratic factors. In
addressing the potential reverse causation of investment on entrepreneurial saving practice in section
6, we will utilize entrepreneur’s Age, Age2 , and Distance to the nearest bank as instruments in order
to capture the utility loss implied by bank transactions costs.
The efficiency of the formal saving practice is denoted with ζF and the efficiency of the informal
saving practice is denoted with ζI , where ζF = 1 > ζI for all I individuals who save informally. Using
equation (12) from the entrepreneurial optimization problem, an entrepreneur i is willing to save
formally if and only if


ρF − ρI = (Ai + (1 − θi )`2 ) 
1 + `2
1
1−θi
ζF
−
1 + `2
1
1−θi
ζI
 > ψi ,
(13)
which would hold if (a) the entrepreneur has a low cost of accessing formal financial institutions and/or
(b) a high enough productivity and/or (c) limited access to borrowing.
We utilize the theoretical argument we derived at equation (13), when we study the reverse
causation of re-investment likelihood on entrepreneurial saving practice in section 6.
13
2.6
Impact Heterogeneity
The entrepreneurial (in)efficiency associated with informal saving practices is expected to be a function
of accessibility to savings. Such accessibility constraints could be related to the repayment structure
for the case of informal saving networks (e.g. ROSCAs) and to household bargaining power for the case
of in-household savings. This implies, for instance, that entrepreneurs with low household bargaining
power would have a lower ζI . The bargaining power of an individual could vary according to the
position of the individual in the household. For instance, due to social norms and pressures female
household members, children, and siblings are naturally at a more disadvantageous position than
males and household heads in terms of claiming from the common resources of the household. They
are less likely to claim money from the common savings pot of the household to finance their liquidity
needs and are therefore less likely to reinvest. We will utilize this intuition when studying impact
heterogeneity in section 6.
2.7
Empirically Testable Hypotheses
In our regression equations we will control for a vector of variables to test the theoretical results we
obtained in propositions 2.1 through 2.3. Specifically, the empirically testable hypotheses resulting
from our model are the following:
1. H0 : Entrepreneurs who save efficiently (high ζ) are more likely to invest.
2. H0 : Entrepreneurs with a high borrowing capacity (high θ) are more likely to invest.
3. H0 : Entrepreneurs with better training, higher education and higher income (high A) are more
likely to invest.
14
3
The Empirical Methodology
We test the hypotheses derived from the theoretical model with a dataset collected from Tanzanian
MSEs by the Financial Sector Deepening Trust of Tanzania. To test whether saving practices affect
the decision to reinvest, we use the binary outcome variable reinvest, which equals 1 if the entrepreneur
invests some of the profits back into business, and estimate the following model
Reinvesti = α + β 0 Si + γ 0 Controlsi + i ,
(14)
where i denotes the entrepreneur, S is a vector of saving practices comprised of dummy variable(s)
which take(s) the value of 1 if the entrepreneur has the corresponding saving practice (see below for
details) and is the error term. Since our dependent variable is binary, we estimate probit models for
all different specifications of (14), and report marginal effects at mean levels for the coefficient estimates
unless we state otherwise. The vector of control variables included in the benchmark model is composed
of an array of entrepreneurial and enterprise characteristics that we discuss in the following.
First, in line with our theoretical model, we control for firms’ past borrowing history. Specifically, Borrowed is a dummy variable which takes the value of 1 if the entrepreneur has ever borrowed
to cover business needs, and it is a proxy for the θi parameter in the theoretical model. Businesses
that have access to external finance are expected to reinvest more frequently even in the absence of
regular entrepreneurial savings.
Second, we use income level, education and business training history of entrepreneurs as proxies
of entrepreneurial productivity Ai . We conjecture that entrepreneurs with a higher household income
can save more and as a result reinvest more often. To control for the income effects, we use self
reported monthly personal income levels.4 Entrepreneurs with a high human capital are expected to
be more committed to business growth, and to have higher rates of earnings retention. We therefore
use the highest level of formal education completed by the respondents, as well as an indicator of
entrepreneurial training, as this should matter for expected business performance and reinvestment
4
Each respondent is asked which income range (e.g. TSHS 35 001 - TSHS 40 000 per month) describes their income
level best. We use the median of that range (e.g. TSHS 37500.5) as the income level of the respondent.
15
behavior.
Third, although they are not discussed in our model, we additionally control for gender and
marital status as previous studies showed that both can influence investment decisions (Iversen et al.,
2006; Ashraf, 2009; de Mel et al., 2009 and Fafchamps et al., 2013). Specifically, we expect female
entrepreneurs to face more claims on their income from spouse and family members. Similarly, married
entrepreneurs might face more claimants on the business profits and might therefore be less likely to
re-invest. Finally, we include sectoral dummies to control for sectoral performance that might explain
reinvestment heterogeneity, as well as regional dummies to control for geographic heterogeneity in
profitability and reinvestment.
We empirically explore the relationship between specific forms of saving and the likelihood
of reinvestment. Specifically, our survey allows us to identify two types of saving practices among
Tanzanian entrepreneurs which we classify as follows:
1. Save formal : This practice includes the entrepreneurs who save their funds at formal financial
institutions such as commercial banks, microfinance institutions or saving & credit cooperatives.
So entrepreneurs who save only formal and save both formal and informal (please see below for
the definitions ) means are considered in this group.
2. Save informal : We consider entrepreneurs who do not save formally in this group.
This separation corresponds to the control-treatment group set-up of many randomized control trials that assess the impact of using formal savings products on household and entrepreneurial
outcomes. In addition, however, our survey allows a finer classification to exploit the considerable
heterogeneity in terms of informal saving practices. Therefore we first divide save informal into two
groups and distinguish individual saving practices and practices involving interaction with other people
as follows:
1. Save informal individually: A large fraction of entrepreneurs in Tanzania save their funds only in
a secret hiding place or piggy bank.5 We classify this behaviour as “informal individual saving”
5
Piggy bank is a coin container.
16
practice.
2. Save informal with others: We classify the practices of saving funds via informal savings clubs,
such as ROSCAs, or moneylenders or within household savers under “saving with others”. We
do not include respondents who also save formally in this group. the entrepreneurs who both
save informal individually and save informal with others are considered in this group.6
To distinguish whether our entrepreneurs save through people living in the household or people
who are not member of a household, we decompose the practice of “Save informal with others” further
into two groups.
1. Save with household members: The group comprises of entrepreneurs who give their funds to
other household members to keep them safe.
2. Save with people outside household : The group contains entrepreneurs who save through ROSCAs
or moneylenders. The entrepreneurs who both save informal with household members and save
informal with people outside household are considered in this group.7
We again conjecture that entrepreneurs in the second group have more control over their savings
than entrepreneurs in the first group, especially if the latter have limited intra-household bargaining
power. In our regression analysis, we will use a dummy variable for each saving practice above (see
Table 1 below for the descriptions) and work with different samples to compare both savers and
non-savers but also different groups of savers in their reinvestment behaviour.
4
The Data
The dataset is based on a novel enterprise survey conducted at the MSE-level in Tanzania. The
survey data was collected by the Financial Sector Deepening Trust Tanzania in 2010 from a nationwide
6
Our results are robust when we create a separate dummy variable for this group having both saving practices and
add them to the regressions.
7
We do not include the respondents having both practices, saving informal both with people outside household and
with household members, to our main regression specifications as only a few respondents (7) do both.
17
representative cross-section of 6,083 micro- and small enterprises. The respondents of the questionnaire
are entrepreneurs with an active business as of September 2010. Table 1 presents both detailed
definitions of the variables and descriptive statistics of the sample.
- Table 1 about here -
The descriptive statistics in Panel A of Table 1 shows that the average number of employees
among Tanzanian MSEs is 1.5 workers, ranging from one (i.e. self-employed) to 80 employees.8 However, 97% of entrepreneurs are self-employed. The median initial capital is about 35 USD and average
monthly sales are 149 USD. The key question which we exploit to capture entrepreneurs’ earnings
retention asks whether the respondent reinvests some of the profits back into business. As we present
in Table 1, 76% of the sample entrepreneurs engage in earnings retention.
The sectoral breakdown in Panel B of Table 1 exhibits substantial variation: 54% and 30% of
the businesses operate in the trade and service sectors, respectively, while 15% of enterprises operate
in manufacturing.
Panel C of Table 1 presents characteristics of entrepreneurs and enterprises. About 50% of
the entrepreneurs in the sample are female, 10% of the entrepreneurs are single. 30% of the sample
entrepreneurs received business related training, and about 87% of the entrepreneurs have less than
completed secondary education. 75% of the enterprises are located in rural areas. The median monthly
personal income of entrepreneurs is 106 USD.9
Panel D of Table 1, finally, presents our variables and descriptive statistics on the financing
patterns of enterprises in our sample. Only 18% of all sample entrepreneurs ever borrowed for business
purposes; 3% of entrepreneurs in the sample borrowed from a bank or MFI, 2% borrowed from a
semi-formal financial institution, such as a SACCO or village bank and 6% borrowed from an informal
source, such as money lenders, savings club or family and friends.
8
The relationship between business owners’s saving and re-investment decisions might be weak in large businesses
because of managerial layers. We test the robustness of our main result by excluding the businesses larger than 10 from
our sample. Estimates reported in Table 3 do not change.
9
This is computed with the average exchange rate for 2010. If using PPP exchange rates, the corresponding median
income would be 288 dollars.
18
Saving is a common habit among the entrepreneurs in our sample. We utilize an extensive
margin question asking whether the entrepreneur saves for business purposes, and distinguish savers
from the rest of the population: 77% of the entrepreneurs in the sample save for business purposes.
However there is considerable heterogeneity among saving practices of Tanzanian entrepreneurs. Informal individual saving is the most popular practice among Tanzanian entrepreneurs. 75% of the
savers save informal-individually whereas around 13% of them save formally. Likewise, 13% of the
savers do not save at a formal financial institution and instead save their funds via people outside the
household such as members of ROSCAs and moneylenders or give them to household members.
Table 2 presents a correlation matrix concerning the variables of interest for our analysis. The
key variables such as “being a saver” and “retaining earnings within the business” exhibit a strong
correlation. However, the sign of the relationship seems to be dependent on the saving practice of the
respondents. In particular saving via others seems to be negatively correlated with firm reinvestment
whereas formal and informal individual savers have higher reinvestment rates. We also note a high
correlation among other firm characteristics, such as borrowing and saving activity.
- Table 2 about here -
5
Saving Practices and Reinvestment: Main Results
Table 3 reports the marginal effects for the benchmark regression. We use heteroscedasticity robust
standard errors and report the standard deviations associated with coefficient estimates in parentheses.
- Table 3 about here -
The results in the first column show that the probability of reinvestment is higher for both
groups of savers compared to non-savers. Specifically, ceteris paribus, the reinvestment probability of
an average Tanzanian MSE who saves informally is around six percentage points higher than for an
entrepreneur who does not save, while the reinvestment probability of an average Tanzanian MSE who
19
saves formally is around nine percentage points higher. We also find that entrepreneurs with access
to formal loans are more likely to reinvest, while formal business training increases the likelihood
of reinvestment in business projects. Female and married entrepreneurs are less likely, while richer
entrepreneurs are more likely to invest. Overall, these results are consistent with our theoretical
predictions as discussed above and the existing literature.
Our empirical analysis, so far, stresses the significance of entrepreneurial savings to foster
entrepreneurial reinvestment in business projects and we confirmed that saving related correlations
are in line with the findings in the literature. In the next step, we focus on our main research question
and we deepen our analysis by studying the implications of saving practices on reinvestment. In order
to test the predictions from our theoretical model, we rank saving practices based on their vulnerability
to consumption temptations - as we discussed above - and investigate the implications of the variations
in saving methods for the probability to reinvest. Specifically, we rank the “within household savers”
as the group for whom the vulnerability to consuming savings is the highest. On the other extreme,
we expect the most committed savers to be “formal savers” due to the highest opportunity cost of
consumption - resulting from the foregone interest income. Finally, comparing “informal individual
savers” with “informal savers with others”, we conjecture that while the redistributive pressure problem
might be lower for the former, there would be a potential inflexibility to withdrawing savings when
needed associated with the latter.
Here we also note that we study our main research question by focusing on specific sub-samples
of savers in order to present the results clearer, and keep the consistency between the samples used
for main estimations, robustness checks and bivariate probit estimates (see below). To show that our
estimates are not biased due to this method, we replicate the analysis by using the entire sample. We
present the results in Table A1 in the Appendix, and show that our estimates are robust.10
The results in column 2 show that “formal savers” are four percent more likely to retain earnings than the “informal savers”. To investigate the effects of individual saving practices on earnings
retention we limit our sample to savers and thus drop respondents who do not save. The results in
10
The only difference between the results concerns the estimate for save with people outside the household. It is
statistically significant at ten percent level due to lower standard error estimates when we use the full sample.
20
column 3 show that entrepreneurs who save with others are less likely to reinvest than entrepreneurs
who save formally.11 Also, entrepreneurs who save informally but individually are not significantly less
likely to reinvest when compared to “formal savers”.
Finally, we focus on the group of respondents who save with others. We independently study the
investment likelihood of household savers and respondents who save outside the household compared
to the reinvestment probability of formal savers. The regression in column (4) keeps only formal
savers and household member savers in our sample, while the regression in column (5) keeps only
formal savers and outside household savers in our sample. In both cases, we gauge the difference
in reinvestment behaviour relative to formal savers. Therefore, the total numbers of observations in
these two regressions are 877 and 774, respectively. Confirming our conjecture, we cannot reject the
null hypothesis that “with household member savers” reinvest less frequently compared to “formal
savers”, at the 5% level. Furthermore, we also show that, although the coefficient estimate of Save
with people outside household variable in the last regression is not significant, the negative coefficient
sign is consistent with the argument that the inflexible withdrawal opportunity of “informal savings”
might be a barrier to earnings retention.
In summary, our baseline empirical results are consistent with our theoretical model showing
that inefficient saving practices lead to lower likelihood of reinvestment. They suggest that informal
saving practices are associated with significantly lower likelihood of earnings retention compared to
formal saving mechanisms. It is important to note that this finding is mainly driven by the difference
in the reinvestment likelihood of within household savers and formal savers, for which the difference
is most pronounced and statistically significant.
In Table 4, we test the robustness of our key result concerning the difference in reinvestment
likelihood between formal and within household savers (see column (4) in Table 3) with respect to
the inclusion of a vector of additional control variables. First, we add specific dummy variables
for different sources of external finance at the start-up of the enterprise: formal, semi-formal and
11
Here we consider entrepreneurs who have both types of informal saving practices, “saving informal individually”
and “saving informal with others” inside “save informal with others” group. When we estimate specification in column3 by adding a separate dummy for individuals having both practices and saving only informal with others together
saving informal individually, estimates for the first two groups including saving practices with others are negative and
statistically significant showing that our results are robust.
21
informal loans. Our indicator for external finance may not capture the potential implications of
access to different sources of finance for reinvestment decisions. Getting loans from a formal financial
institution might require a bank account and facilitate formal entrepreneurial savings. However, none
of the external financing variables that we include have significant explanatory power for reinvestment
likelihood. Second, we control for entrepreneurial types by utilizing the answers to the following survey
question: “why did you go to business? ”12 As evidenced in the previous literature (Bruhn and Zia 2011),
transformational type entrepreneurs are expected to have higher rates of investment profitability and
earnings retention rate compared to survival type entrepreneurs. While we do not report the individual
dummy variables, some variables enter significant at the 5% level. Third, we add dummy variables to
control for the type of the activity the business conducts. The activity of the business (e.g. buying and
re-selling; buying, adding value and re-selling, providing a service etc.) may change the definition of
re-investment for business owner and timing of the reinvestment. For instance, they may be different
for a restaurant owner and a market vendor. To control for this factor, we include answers to the
question “what does your business do?” as dummy variables.13 The estimates for the variables are
jointly significant at the 1 percent level. To economize on space we do not report estimates, and
they are available upon request. Fourth, we include the size of the logarithm of the initial start-up
capital, the logarithm of current sales per employee, the logarithm of the duration of business and the
logarithm of number of workers since these size gauges are expected to determine the growth potential
of a business- and hence the profitability of reinvestment. We also control for rural vs. urban location
of the enterprise, as the accessibility to infrastructure might affect expectations and drive variations
in reinvestment rates. Including all of these control variables does not affect our key empirical finding.
Finally, in column (2) we replace the region fixed effects with district fixed effects to ensure that
we are capturing geographical variations well enough that could explain the probability of reinvestment.
While our sample becomes smaller, our findings remain.14
12
Entrepreneurs selected from a list of statements to indicate why they went into business. Multiple choices were
available. The answers include: I was fired / lost/retrenched from a previous job; I couldn’t find a job elsewhere; To
support me / my family; To try out a business idea; I believe I can make more money working for myself than for
someone else; I had nothing else to do/no other means of survival/no better option; parents / relatives were in business;
I saw a good opportunity; I have always wanted my own business; I was encouraged by friends and relatives; I needed
to supplement my income; Others, please specify.
13
We include 5 separate dummy variables for the businesses buying and selling goods; buying, adding value and selling
goods; making and selling goods; providing service; and other activities including agricultural ones.
14
Note that when we include district fixed effects the total number of observations in the regression decreases to 650
because some districts are excluded from the regression in Probit estimations due to perfect prediction. Our estimates
22
- Table 4 about here -
6
Saving Choice, Reverse Causality, and Heterogeneity
While controlling for other enterprise and entrepreneurial characteristics reduces the risk that the
relationship between savings patterns and the likelihood of reinvestment is a spurious one, we cannot
exclude the possibility that our relationship is driven by other sources of endogeneity, including reverse
causation. As we show in our theoretical model, entrepreneurs who are more willing to reinvest might
look for saving practices that support their investment efforts. In the following, we focus on the sample
of formal and within-household savers once more since our key result from the empirical analysis of
section 5 is that “within household savers” are less likely to re-invest than “formal savers”. Focusing
on only one sub-sample also has a methodological advantage as we need fewer exogenous determinants
to identify the relationship. For this sample, we investigate the relationship between entrepreneurial
saving choices and characteristics, and then offer a test to alleviate endogeneity concerns.
To investigate the determinants of saving choice, we replace the dependent variable reinvest with
save within household in (14) and regress it on our list of control variables as well as on two additional
measures denoted by ψi in our theoretical model: Age of the entrepreneur and distance to bank. Age
increases the bargaining power of the entrepreneur within the household and this implies a U-shaped
relationship between age and the choice within household saving. On the one hand, agents are less
likely to be forced to save within household as they get older. On the other hand, when they reach an
age giving them enough power to protect their savings within the household, they may be more likely
to save with household members. The distance to the nearest bank is expected to increase accessibility
of “formal savings services”. We estimate two models with two different measures of distance to formal
financial institutions. The first one is a subjective distance measure constructed by using the question
from the enterprise survey: Is there any bank branch in one hour walking distance to your house?
However, there might be a concern regarding the subjective measure, as entrepreneurs who search
for formal savings instruments are also those who are more likely to know of the existence of a bank
are robust when we estimate the same model with OLS and do not lose any observations.
23
in the close proximity. Therefore, the correlation between the search intensity and some unobserved
characteristics may bias our results. For this reason, we estimate a model with an additional objective
distance measure, the logarithm of ward level minimum distance to the closest bank branch, MFI or
ATM in 2013 which we constructed using data from the Financial Services Map.15
Table 5 reports the marginal effect from probit estimations for the saving practice choice. In
columns (1) and (2) we present the results for models including subjective and objective measures
respectively. As we conjecture, the likelihood of saving with household members is higher when
entrepreneurs are closer to banks. Moreover, as the age of the entrepreneur increases, he or she is less
likely to save with household members. The positive coefficient (0.00038) on the square of age indicates
that the age saving with household members practice relationship is non-linear and U-shaped. As the
age of the entrepreneur increases, the impact of the age on the saving practice decreases, and getting
older increase the probability of saving with household members after the age of 52. The rest of the
estimates are also in line with our theory. Entrepreneurs who have access to external finance and
entrepreneurs with higher education, better training or high income are more likely to save formally.
Finally, female entrepreneurs seem more likely to save in formal institutions - perhaps to escape from
redistributive pressures. Also, non-married entrepreneurs are more likely to save formally.
- Table 5 about here -
To circumvent the endogoneity concerns, we use an instrumental variable methodology which
makes use of the determinants of saving practice choice. Since our dependent and main explanatory
variables are binary, we use a system approach, and utilize the age of the entrepreneur and her distance
to the nearest bank as instruments in a nonlinear recursive bivariate probit model.16 Specifically the
15
We use data from the Financial Services Map for Tanzania. This data set gives geographic coordinates of bank
branches, MFIs and ATMs in 2013 acrossTanzania. We match these data with the existing geographic coordinates of
the wards from which entrepreneurial data are collected. Then we calculate the distance of the wards to each financial
unit and pick the minimum distance.
16
We also estimate the same model by using the 2SLS method. We have the same expected signs for the variables
of interest but the coefficient estimates are bigger and imprecise as the variance increases. We believe this is because
both the dependent and independent variables of interest are binary. Chibus et al. (2012) suggests 2SLS may give very
different results and imprecise estimates if the number of observations is lower than 5000 (in our case it is 877).
24
model is formulated as follows:
Reinvesti = φ + δSavehouseholdi + η 0 Controlsi + σi ,
Savehouseholdi = λ + µ0 Zi + π 0 Controlsi + ui .
(15)
(16)
We assume that error terms σi and ui are distributed via bivariate normal distribution. So, E[σi ] = 0,
E[ui ] = 0 and cov[σi , ui ] = µ. We identify the system by using the vector Z which includes the
distance to bank measure and age of the entrepreneur as well as its square and use a similar set of
controls as in the main specifications.17 Table 6 shows the results. Before presenting the estimates of
the bivariate probit model, in columns (1) and (2), we test in unreported regressions the exogeneity of
our instruments. As standard overidentification tests for 2SLS are not available for Bivariate Probit
estimation, we utilize an informal test procedure commonly used in the empirical literature (e.g. Egger
et al., 2011; Booker et al., 2013): We introduce the instruments into the benchmark model, where
we show that none of the instruments has explanatory power for the probability to reinvest. We
also test the joint significance of our exogenous variables in the bivariate probit model: they are
jointly significant at the 1 percent level (Chi-square>20 and p-value<0.001 for both specifications). In
columns (3) and (4) we present the recursive bivariate-probit estimates by using age in both models,
but two different distance measures as our instruments. Also, Table A2 in the Appendix shows detailed
estimation results for the model, including the control variables.
- Table 6 about here -
The instrumental variable estimations reported in columns (3) and (4) of Table 6 confirm our
results. The coefficient estimate of save with household member remains negative and significant for
both instrument sets. Different measures of distance produce similar results thereby minimizing the
concerns regarding the validity of the distance-to-bank proxies. We also note that the estimates for the
exogenous variables have the expected signs. The probability to save in the household decreases as the
proximity to bank decreases and entrepreneur gets older. We have also another important evidence
17
We do not use sector dummies in the bivariate probit estimations since our model does not converge. However, not
using sector dummies does not change our results since our main results shown in Table 3 are robust when we do not
control for them.
25
minimizing the endogeneity concerns: For both estimations, the estimated cross correlation coefficient,
µ̂, is not statistically significant; thus we do not reject the null hypothesis that Reinvestmenti and ui
are uncorrelated, and reinvestment is exogenous for saving practice choice.
As we discussed in section 2.6, we expect heterogeneous reinvestment responses with respect to
the within-household saving practice. Therefore, in order to deepen our analysis and strengthen our
identification, we present a set of impact heterogeneity results in Table 7. Specifically, we compare
the reinvestment behaviour of entrepreneurs who save with household members with the reinvestment
behaviour of entrepreneurs who use formal savings mechanisms across the following two sample splits.
First, we split the sample into female and male entrepreneurs. Theory and empirical evidence suggests
that social constraints on accessibility of saved funds is higher for women compared to men. Second,
we split the sample into entrepreneurs that are household heads and entrepreneurs that are spouses,
children or siblings. We expect the social constraints to be less strong for household heads.
The results in Table 7 confirm the differential relationships between household savings and
reinvestment decisions. The results reveal that the marginal effects of Save with household members
on reinvestment are larger - and more significant - for female and non-head family members. While the
negative relationship between saving within the household and reinvestment decisions are significant
at least at the 10% level for all groups, the economic significance is large for female, non-household
heads. Supporting our theoretical predictions, this result implies that entrepreneurs who are in disadvantageous positions in their households are more negatively affected from inefficient saving practices.
- Table 7 about here -
7
Conclusion
Past research has identified several factors that are important for entrepreneurial investment in developing countries. In this study, we explored how different entrepreneurial saving practices - i.e. saving
via formal financial institutions, individually (under the mattress), within the household or within
informal arrangements, such as ROSCAs - are related with the likelihood of reinvestment. To this
26
end, we used a novel survey data set collected from MSEs in Tanzania and distinguished multiple saving practices of entrepreneurs as well their earnings retention behaviour. We motivated our empirical
research with a simple theoretical model that shows how different saving practices can influence investment decisions. We have three key empirical results. First, we show that saving and the probability
of reinvestment are significantly correlated. Second, we provide evidence that entrepreneurs who save
by giving funds to other household members are less likely to reinvest than formal savers. Third, we
document that the difference in the likelihood of reinvestment across saving practices is significantly
higher for those entrepreneurs who potentially have low bargaining power in the household.
Our findings suggest that the entrepreneurs who need to protect their savings from consumption commitments of other household members may benefit most from the introduction of formal
saving instruments in low income areas. Therefore, from a development policy perspective, targeting
entrepreneurs who have low decision power in the household and facilitating their access to formal
saving instruments could be thought as a priority. Our results have important implications for the
interactions between enterprise performance and financial access as well. Enterprises that exploit reinvestment opportunities are expected to be more likely to sustain higher productivity levels and survive
more often. Access to efficient saving mechanisms in this respect could be key to facilitate enterprise
performance in financially developing societies.
Our research raises also some new issues regarding the implications of savings practices of
entrepreneurs. First, why do savers inside households not open a bank account to save? Although we
implicitly show proximity to banks as an important factor to save in a formal account, identification
of all factors is not in the scope of this study. Second, what is the exact role of pressure inside the
household that does not allow earnings retention? These important questions we leave to future work.
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30
31
Description
Equals to 1 if respondent re-invest some of the profit back to business, 0 otherwise
Number of employees business has [including owner]
Logarithm of initial capital of the business, in Tanzanian Shillings
Number of companies
3291
1841
931
20
Description
Education level of the respondent, [0 none-6 university]
Equals to 1 if respondent is female, 0 otherwise
Equals to 1 if respondent is single, 0 otherwise
Equals to 1 if respondent has no business related training, 0 otherwise
Equals to 1 if respondent lives in a rural area, 0 otherwise
Logarithm of personal income level of the respondent in Tanzanian Shillings
Equals to 1 if there is a bank within a one
hour walk from the home of the respondent, 0 otherwise
Minimum distance of the ward entrepreneur lives to the nearest ATM,
bank branch or MFI, in logarithms, at ward level]
Age of the respondent
Description
Equals to 1 if respondent saves for business purposes, 0 otherwise
Equals to 1 if respondent saves in a bank account, MFI or SACCO, 0 otherwise
Equals to 1 if respondent saves but not in a bank account, MFI or SACCO
and, 0 otherwise
Equals to 1 if respondent saves in a secret hiding place or piggy bank
and does not save via other means, 0 otherwise
Equals to 1 if savehousehold or saveouthousehold equals to 1 and does not save
formally, 0 otherwise
Equals to 1 if respondent save via by giving it to a household member to keep it
safe and does not save formally, 0 otherwise
Equals to 1 if respondent save via by giving it to a non household member
or merry go-round and does not save formally, 0 otherwise
Equals to 1 if respondent has ever taken a loan/ borrowed money for business
purpose, 0 otherwise
Equals to 1 if respondent took a to set up or take over the business from a bank
or MFI, 0 otherwise
Equals to 1 if respondent took a credit from an employer , SACCO,
Village Bank, local government schemes or donor/NGO to set up
or take over the business , 0 otherwise
Equals to 1 if respondent took a to set up or take over the business from
family, friends, savings club, money lender or supplier, 0 otherwise
6083
6083
6083
6083
6083
6083
6083
6083
6083
6083
Obs
6083
6083
6083
583
Obs
6083
6083
6083
%
54.1
30.3
15.3
0.3
Obs
6077
6083
6083
6083
6083
5868
6083
0.06
0.06
0.02
0.03
0.18
0.05
0.06
0.10
0.57
36.84
Mean
0.77
0.10
0.67
0.24
0.24
0.13
0.16
0.38
0.20
0.24
0.30
0.49
10.6
S.D.
0.42
0.30
0.47
1.78
S.D.
0.89
0.50
0.29
0.46
0.44
1.15
0.46
Mean
2.00
0.50
0.10
0.70
0.75
11.94
0.30
2.04
S.D.
0.43
1.61
2.21
Mean
0.76
1.47
10.62
0
0
0
0
0
0
0
0
0
16
Min
0
0
0
-4.35
Min
0
0
0
0
0
9.90
0
Min
0
1
0
Notes: The table shows the varibles definitions and descriptive statistics for the selected variables used in the analysis. Obs., Mean. S.D., Min. and Max. shows the number observation
for, sample averagem standard deviation, minimum, and maximum of the corresponding variable.
Informal loan
Semi formal loan
Formal loan
Borrowed
Save with people outside household
Save with household members
Save informal with others
Save informal individually
Age
Panel D: Finance variables
Save
Save formal
Save informal
Min. distance to ATM, bank branch, or MFI
Panel A: Firm characteristics
Reinvestment
Employee
Initial capital
Panel B: Sectoral breakdown of firms
Trade
Service
Manufacturing
Other
Panel C: Entrepreneur
Education
Female
Single
No training
Rural
Income
Bank branch within one hour walking distance
Table 1: Descriptive statistics for the main variables
1
1
1
1
1
1
1
1
1
91
Max
1
1
1
6.12
Max
6
1
1
1
1
15.20
1
Max
1
80
25.33
Save
1
0.18*
0.62*
0.18*
0.14*
0.12*
0.12*
0.13*
0.00
0.00
-0.06*
0.12*
Save formal
1
-0.38*
-0.11*
-0.08*
-0.07*
0.30*
0.24*
-0.05*
0.00
-0.08*
0.19*
Save informal individually
1
-0.39*
-0.29*
-0.25*
-0.09*
-0.04*
0.03*
0.01
0.02
-0.02
Borrowed
Save with people outside household
Save with household members
Save informal with others
1
0.74* 1
0.64* -0.03* 1
0.03* -0.01 0.05* 1
0.02
0.02
0.01 0.17*
0.00
-0.10* 0.11* 0.03*
-0.01 0.00
-0.01 -0.04*
-0.04* -0.04* -0.01 -0.10*
0.01
0.02
-0.02 0.12*
Female
Education
1
-0.10* 1
0.07* 0.01
-0.12* 0.00
0.19* -0.24*
Single
1
0.00 1
-0.02 0.00
No training
32
1
Notes: The table shows the pairwise correlation coefficient among selected variables shown in first row and first column of the Table. The detailed variable definitions are given in Table 1.
* indicates the estimate is statistically significant at least 5 percent level.
1
0.09*
0.07*
0.05*
-0.03*
-0.04*
0.00
0.06*
0.07*
-0.06*
0.02
-0.02
0.13*
Reinvestment
Reinvestment
Save
Save formal
Save informal individually
Save informal with others
Save with other household members
Save with people outside household
Borrowed
Education
Female
Single
No training
Income
Table 2: Correlation Matrix: Pairwise correlations among selected variables
Income
Table 3: Regressions for reinvestment and saving/saving practices relationship
Save formal
Save informal
(1)
0.09***
(0.02)
0.06***
(0.01)
(2)
(3)
(4)
-0.04*
(0.02)
Save informal individually
-0.03
(0.02)
-0.09***
(0.03)
Save informal with others
Save with household members
-0.12***
(0.04)
Save with people outside household
Borrowed
(5)
-0.04
(0.03)
0.04
(0.03)
-0.01
(0.01)
-0.04
(0.03)
0.03
(0.04)
-0.06**
(0.03)
0.05***
(0.01)
0.04**
(0.02)
0.01
(0.01)
-0.03**
(0.01)
0.04**
(0.02)
-0.03***
(0.01)
0.03***
(0.01)
0.05*** 0.05***
(0.02)
(0.02)
0.01
0.01
(0.01)
(0.01)
-0.03** -0.03***
(0.01)
(0.01)
0.03
0.03
(0.02)
(0.02)
-0.04*** -0.04***
(0.01)
(0.01)
0.02**
0.02**
(0.01)
(0.01)
0.05
(0.03)
-0.01
(0.01)
-0.07**
(0.03)
0.06
(0.04)
-0.07**
(0.03)
0.03**
(0.01)
Observations
Sample
5,803
All
4,499
Savers
4,499
Savers
Base category
No
saving
Save
formal
Save
formal
877
774
Formal and Formal and
household
others
savers
savers
Save
Save
formal
formal
Education
Female
Single
No training
Income
Notes: This table shows our baseline estimation results for the relationship between saving practices, control variables and reinvestment
likelihood. The detailed variable definitions are given in Table 1. Reinvestment is the dependent variable in the estimations. We estimate
Probit models for all specifications in columns 1 to 5 and report marginal effects estimates at mean values for all variables and robust
standard errors in parentheses. To control for unobserved regional and sector level fixed effects, we add sector and and region dummies
to all estimations. The details of the estimations in the columns 1 to 5 are as follows:
- In column 1, we compare the reinvestment likelihood of formal and informal savers with non-savers. We use our entire sample for this
estimation. The estimate for Save formal (informal) shows the difference between the impact of Save formal (informal) and not saving
(base category) on reinvestment likelihood.
- In column 2, we compare the reinvestment performance of informal savers with formal saver by restricting our sample to only savers
and dropping the respondents who do not save. The estimate for Save informal shows the difference between the impact of Save informal
and Save formal (base category) on reinvestment likelihood.
- In column 3, we disentangle informal saving practices to saving informal individually and saving informal with others by adding separate
dummies for each group. The estimate for Save informal individually (Save informal with others) shows the difference between the impact
of Save informal individually (Save informal with others) and Save formal (base category) on reinvestment likelihood.
- In column 4, we compare reinvestment likelihood of household savers with formal savers by keeping only formal savers and household
member savers in our sample. The estimate for Save informal with household members shows the difference between the impact of Save
informal with household members and Save formal (base category) on reinvestment likelihood.
- In column 5, we compare reinvestment likelihood of outside household savers with formal savers by keeping only formal savers and
outside household savers in our sample. The estimate for Save informal with people outside household shows the difference between the
impact of Save informal with people outside household and Save formal (base category) on reinvestment likelihood.
p<0.1. ** p<0.05. *** p<0.01
33
Table 4: Estimates for reinvestment and save with household members relationship-additional control
variables
Save with household members
Formal Loan
Semi formal loan
Informal loan
Initial capital stock
Sales per worker
Rural
Size
Duration
Observations
Entrepreneurial dummies
Activity Dummy
Region FE
District FE
(1)
-0.07**
(0.03)
0.02
(0.05)
-0.11
(0.09)
-0.12
(0.07)
0.02**
(0.01)
-0.02
(0.01)
0.03
(0.03)
0.04
(0.03)
0.02
(0.01)
(2)
-0.18***
(0.05)
872
Yes
Yes
Yes
No
650
No
No
No
Yes
Notes: This table test the robustness of the estimates for Save informal with household members shown in Column 4 of Table 3 to the
inclusion of additional control variables. Reinvestment is the dependent variable in the estimations. We estimate Probit models for the
in columns 1 and 2 and report marginal effects estimates at mean values for all variables and robust standard errors in parentheses.
In addition to the variables shown in the Table, we also add Borrowed, Education, Female, Single, No training, Income, and region
dummies to the estimated models as control variables. The detailed definitions of these variables are given in Table 1. The sample used
for estimations include only formal savers and informal savers with household members. Save formal is the base category for Save with
household member estimates in both estimations. The details of the estimations in the columns 1 and 2 are as follows:
- In column 1, we test whether the estimate for Save informal with household members is robust with respect to the inclusion of a vector of
additional variables in addition to the listed variables above and region dummies. These additional control variables are as follows. Formal
loan, Semi-formal loan, and Informal loan are dummy variables equals to 1 if the respondent received credit from formal, semi-formal and
informal resources. Initial capital stock is the logarithm of the value of capital in U.S dollars invested to establish the business. Sales
per worker is the sales divided by number of permanent workers of the business the respondent owns in logarithms. Rural is a dummy
variable equals to 1 if entrepreneur lives at a rural area. Size is the number of permanent employees working in the business of the
entrepreneur in logarithms. Duration is the logarithm of the number of years that the entrepreneur is doing the business. Entrepreneurial
dummies consist of seven binary variables created for the listed answers to the question why did you go into business?. The list comprises
statements I was fired / lost/retrenched from a previous job; I couldn’t find a job elsewhere; To support me / my family; To try out a
business idea; I believe I can make more money working for myself than for someone else; I had nothing else to do/no other means of
survival/no better option; parents /relatives were in business; I saw a good opportunity; I have always wanted my own business; I was
encouraged by friends and relatives; and I needed to supplement my income as an answer. Activity dummies consist of 5 dummy variables
indicating whether the business owned by the respondent buy and sell goods; buy, add value and sell goods; make and sell goods; provide
services; or do other activities including agricultural ones.
- In column 2, we test whether the estimate for Save informal with household members is robust with respect to replacing region dummies
with district dummies. Since there is no reinvestment variation at some district the number of observations is lower for this estimation.
* p<0.1. ** p<0.05. *** p<0.01
34
Table 5: The estimates for save with household members vs. formal saving choices
(1)
-0.13***
(0.04)
Bank branch withing
one hour walking distance
Min. distance to
ATM, bank branch, or MFI
Age
Age2
Borrowed
Education
Female
Single
No training
Income
Observations
(2)
-0.04***
(0.01)
0.00***
(0.00)
-0.32***
(0.03)
-0.12***
(0.02)
-0.10**
(0.04)
-0.14**
(0.06)
0.02
(0.04)
-0.10***
(0.02)
0.04***
(0.01)
-0.04***
(0.01)
0.00***
(0.00)
-0.32***
(0.04)
-0.12***
(0.03)
-0.10**
(0.04)
-0.14*
(0.07)
0.01
(0.04)
-0.10***
(0.02)
877
797
Notes: This table shows the estimates for the determinants of the choice Save informal with household members vs. Save formal. Save
informal with household members is the dependent variable, and Save formal is the base category for Save with household member
estimates. The definitions for the variables are given in Table 1. We report marginal effects estimates at mean values for all estimations
from Probit estimations and robust standard errors are in parentheses. The sample used for estimations include only formal savers and
informal savers with household members. We additionally control for region fixed effects by adding region dummies to the estimations.
The details of the estimations in column 1 and 2 are as follows:
- In column 1, we use Bank branch within one hour walking distance as our distance formal banking services measure.
- In column 2, we use Minimum distance to ATM, bank branch, or MFI as our distance formal banking services measure.
p<0.1. ** p<0.05. *** p<0.01
35
Table 6: Tests for exogeneity of instruments and Bivariate Probit Estimates for save with household
member
Save with household members
Bank branch within one hour
walking distance
Min. distance to ATM, bank
branch, or MFI
Age
Age2
Exogeneity
(1)
-0.11***
(0.04)
0.00
(0.03)
0.01
(0.01)
0.00
(0.00)
checks
(2)
-0.10***
(0.04)
Bivariate Probit Estimates
(3)
(4)
-0.20**
-0.20**
(0.10)
(0.10)
-0.01
(0.01)
0.01
(0.01)
0.00
(0.00)
µ̂
Observations
Exogenous distance measure
877
-
797
-
Methodology
Probit
Probit
0.23
(0.23)
0.27
(0.24)
877
Bank branch
within one hour
walking distance
Bivariate Probit
797
Min. distance
to ATM, bank
branch, or MFI
Bivariate Probit
Notes:This table shows the bivariate probit estimates for Save informal with household members and summarize our informal exogeneity
test for the excluded instruments. Reinvestment is the dependent variable for the estimates, and Save formal is the base category for
Save with household member estimates. The sample used for estimations include only formal savers and informal savers with household
members. We report marginal effect estimates of Save with household members at mean values, robust standard errors in the parentheses
at columns 1 and 3, and clustered robust standard errors at ward level in the parentheses in columns 2-4. We also control for Borrowed,
Education, Female, Single, No training, Income, and region dummies in all models. The detailed variable definitions are given in Table
1. The details of the estimations shown in Column 1 to 4 are as follows:
- In column 1, we informally test whether our instruments, Bank branch within one hour walking distance, Age, and Age2 do not have a
direct impact on reinvestment likelihood in order to test the exogeneity of the instruments that will be used in Column 3 of the Table. If
the estimates are not statistically significant than it may imply that they do not have direct impact on reinvestment but have an impact
only through saving practice choice. Marginal effect estimates at mean values from Probit estimations and robust standard errors in the
parenthesis are reported.
- In column 2, we informally test whether our instruments, Minimum distance to ATM, bank branch, or MFI, Age, and Age2 do not
have a direct impact on reinvestment likelihood in order to test the exogeneity of the instruments that will be used in Column 4 of the
Table. If the estimates are not statistically significant than it may imply that they do not have direct impact on reinvestment but have
an impact only through saving practice choice. Marginal effect estimates at mean values from Probit estimations and robust standard
errors in the parenthesis are reported.
- In column 3, we report the marginal effect estimates for Save informal with household members from Bivariate Probit estimation. Robust
standard errors are in parenthesis. Our excluded instruments for saving practice choice between Save with household members and Save
formal are Bank branch within one hour walking distance, Age, and Age2 .µ is the correlation estimate for the error terms from model
15 and 16.
- In column 4, we report the marginal effect estimates for Save informal with household members from Bivariate Probit estimation.
Robust standard errors are in parenthesis. Our excluded instruments for saving practice choice between Save with household members
and Save formal are Minimum distance to ATM, bank branch, or MFI, Age, and Age2 . µ is the correlation estimate for the error terms
from model 15 and 16.
p<0.1. ** p<0.05. *** p<0.01
36
Table 7: Heterogeneity in the effect of saving with household members on reinvestment
Male
Gender
Female
Position in the household
Other (Child,
Head
spouse, sibling etc)
Save with
household members
-0.12**
(0.05)
-0.22***
(0.08)
-0.22***
(0.08)
-0.16***
(0.06)
Observations
402
275
213
441
Notes: This table summarizes the heterogeneity in the estimates for Save informal with household members for the benchmark estimate
shown in column 4 of Table 3. To test the heterogeneity, we estimate the models in specific subsamples determined acccording to the
respondent characteristics (please see below). Reinvestment is the dependent variable in the estimations, and Save formal is the base
category for Save informal with household member estimates. The sample used for estimations include only formal savers and informal
savers with household members. We report marginal effect estimates of the estimates at mean values from Probit estimations and robust
standard errors in the parentheses. We also control for Borrowed, Education, Female, Single, No training, Income, sector and region
dummies in all models. The detailed variable definition are given in Table 1. Since there is no reinvestment variation at some subsamples,
the number of observations are lower for this estimation, and we therefore estimate all models for the same subsamples where there is
variation in our reinvestment variables. The details of the estimations shown in Gender and Position in the household columns are as
follows:
- In Gender column, we estimate the model for the male and female respondents separately
- In Position in the household column, we separately estimate the model for the respondents who are household heads and others (not
households such as child, spouse, parent, etc. of the household head).
p<0.1. ** p<0.05. *** p<0.01
37
Appendix
38
Table A1: Estimates for reinvestment and saving/saving practices relationship by using full sample
VARIABLES
Save formal
Save informal
(1)
reinvest
(2)
reinvest
(3)
reinvest
0.09***
(0.02)
0.06***
(0.01)
0.09***
(0.02)
0.09***
(0.02)
0.07***
(0.01)
0.01
(0.02)
0.07***
(0.01)
Save informal individually
Save informal with others
Save with household members
0.04**
(0.02)
0.01
(0.01)
-0.03**
(0.01)
0.04**
(0.02)
-0.03***
(0.01)
0.03***
(0.01)
0.04**
(0.02)
0.01
(0.01)
-0.03**
(0.01)
0.04**
(0.02)
-0.03***
(0.01)
0.03***
(0.01)
-0.01
(0.02)
0.04
(0.03)
0.04**
(0.02)
0.01
(0.01)
-0.03**
(0.01)
0.04**
(0.02)
-0.03***
(0.01)
0.03***
(0.01)
5,803
All
p-values
0.0723
-
5,803
All
p-values
0.1819
0.0011
-
5,803
All
p-values
0.1763
0.0004
0.0662
Save with people outside household
Borrowed
Education
Female
Single
No training
Income
Observations
Sample
Hypothesis
H0 : Save formal-Save
H0 : Save formal-Save
H0 : Save formal-Save
H0 : Save formal-Save
H0 : Save formal-Save
informal=0
informal individually=0
informal with others=0
with household members=0
with people outside household=0
Notes: This table shows the baseline estimation results for the relationship between saving practices, control variables and reinvestment
likelihood by using the full sample for each estimation presented in columns 1 to 3. The detailed variable definitions are given in Table 1.
Reinvestment is the dependent variable in the estimations. We estimate Probit models for all specifications and report marginal effects
estimates at mean values for all variables and robust standard errors in parentheses. To control for unobserved regional and sector level
fixed effects, we add sector and and region dummies to all estimations. The details of the estimations in the columns 1 to 3 are as
follows:
- In column 1, we compare the reinvestment likelihood of formal and informal savers with non-savers. The estimate for Save formal
(informal) shows the difference between the impact of Save formal (informal) and not saving (base category) on reinvestment likelihood.
- In column 2, we disentangle informal saving practices to saving informal individually and saving informal with others by adding
separate dummies for each group. The estimate for Save informal individually (Save informal with others) shows the difference between
the impact of Save informal individually (Save informal with others) and Not saving (base category) on reinvestment likelihood.
- In column 3, we disentangle save informal with other to save informal with household members and save informal with people outside.
The estimate for Save informal with household member (people outside) shows the difference between the impact of Save informal with
household members (people outside) and Not saving (base category) on reinvestment likelihood.
We test the difference of the impacts of difference saving practices from Save formal at the bottom of the Table. H0 indicates the null
hypothesis of the test. p-values for the t-test are given in corresponding rows and columns.
* p<0.1. ** p<0.05. *** p<0.01
39
Table A2: Bivariate Probit Estimates
(1)
(2)
Dependendent variable:
Reinvestment Save with
household members
Save with household members -0.81**
(0.37)
Bank branch withing
-0.39***
one hour walking distance
(0.12)
Min. distance to
Atm, bank branch, or MFI
Age
-0.12***
(0.03)
2
Age
0.00***
(0.00)
Borrowed
0.07
-0.98***
(0.16)
(0.12)
Education
-0.07
-0.33***
(0.06)
(0.07)
Female
-0.24**
-0.27**
(0.12)
(0.12)
Single
0.25
-0.47**
(0.18)
(0.22)
No training
-0.32***
0.05
(0.12)
(0.12)
Income
0.08
-0.28***
(0.06)
(0.05)
Constant
0.05
8.51***
(1.07)
(0.97)
Observations
877
877
(3)
(4)
Reinvestment Save with
household members
-0.86**
(0.41)
0.05
(0.17)
-0.07
(0.07)
-0.31***
(0.12)
0.25
(0.20)
-0.36***
(0.14)
0.06
(0.06)
0.56
(1.23)
797
0.10***
(0.04)
-0.11***
(0.04)
0.00***
(0.00)
-0.97***
(0.13)
-0.34***
(0.07)
-0.28**
(0.13)
-0.44*
(0.24)
0.04
(0.12)
-0.27***
(0.05)
8.08***
(1.08)
797
Notes: This table shows the detailed bivariate probit estimates for the columns 3 and 4 of Table 5. We report bivariate probit estimates
for all estimations. Robust standard errors for columns 1 and 3 and clustered robust standard errors at ward level in columns 2-4 are in
parentheses. We use the sample for Formal Savers and Household Savers in all estimations, and Save formal is the base category for Save
informal with household members. We additionally control for region dummies in the estimations. The details of columns 1 to 4 are as
follows:
- In column 1, we present the bivariate probit estimates of model (15) which is jointly estimated with model (16) using Bank branch
within one hour walking distance as the distance measure.
- In column 2, we present the bivariate probit estimates of model (16) using Bank branch within one hour walking distance as the distance
measure and jointly estimated with model (15).
- In column 3, we present the bivariate probit estimates of model (15) which is jointly estimated with model (16) using Minimum distance
to ATM, bank branch, or MFI as the distance measure.
- In column 2, we present the bivariate probit estimates of model (16) using using Minimum distance to ATM, bank branch, or MFI as
the distance measure and jointly estimated with model (15).
p<0.1. ** p<0.05. *** p<0.01
40